Tag Archives: Mobile

I was particularly pleased to see this study appear in the top-tier journal, Nature. (Thanks to my colleague Sarah Vieweg for flagging). Earlier studies have shown that “human communications are both temporally & spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness.” In this new study, the authors analyze crisis events using country-wide mobile phone data. To this end, they also analyze the communication patterns of mobile phone users outside the affected area. So the question driving this study is this: how do the communication patterns of non-affected mobile phone users differ from those affected? Why ask this question? Understanding the communication patterns of mobile phone users outside the affected areas sheds light on how situational awareness spreads during disasters.

The graphs above (click to enlarge) simply depict the change in call volume for three crisis events and one non-emergency event for the two types of mobile phone users. The set of users directly affected by a crisis is labeled G0 while users they contact during the emergency are labeled G1. Note that G1 users are not affected by the crisis. Since the study seeks to assess how G1 users change their communication patterns following a crisis, one logical question is this: do the call volume of G1 users increase like those of G0 users? The graphs above reveal that G1 and G0 users have instantaneous and corresponding spikes for crisis events. This is not the case for the non-emergency event.

“As the activity spikes for G0 users for emergency events are both temporally and spatially localized, the communication of G1 users becomes the most important means of spreading situational awareness.” To quantify the reach of situational awareness, the authors study the communication patterns of G1 users after they receive a call or SMS from the affected set of G0 users. They find 3 types of communication patterns for G1 users, as depicted below (click to enlarge).

The graphs below (click to enlarge) show call volumes by communication patterns 1 and 2. In these graphs, Pattern 1 is the orange line and Pattern 2 the dashed purple line. In all three crisis events, Pattern 1 (call backs) has clear volume spikes. “That is, G1 users prefer to interact back with G0 users rather than contacting with new users (G2), a phenomenon that limits the spreading of information.” In effect, Pattern 1 is a measure of reciprocal communications and indeed social capital, “representing correspondence and coordination calls between social neighbors.” In contrast, Pattern 2 measures the dissemination of the “dissemination of situational awareness, corresponding to information cascades that penetrate the underlying social network.”

The histogram below shows average levels of reciprocal communication for the 4 events under study. These results clearly show a spike in reciprocal behavior for the three crisis events compared to the baseline. The opposite is true for the non-emergency event.

In sum, a crisis early warning system based on communication patterns should seek to monitor changes in the following two indicators: (1) Volume of Call Backs; and (2) Deviation of Call Backs from baseline. Given that access to mobile phone data is near-impossible for the vast majority of academics and humanitarian professionals, one question worth exploring is whether similar communication dynamics can be observed on social networks like Twitter and Facebook.

Disaster responders cannot be everywhere at the same time, but the crowd is always there. The same is true for health care professionals such as critical care paramedics who work with an ambulance service. Paramedics cannot be posted everywhere. Can crowdsourcing help? This was the question posed to me by my colleague Mark who overseas the ambulance personnel for a major city.

Take Sudden Cardiac Arrest (SCA), for example. SCA’s account for an estimated 325,000 deaths each year in the US—one person every two minutes. Survival rates nationally are less than 8%. But Cardio-Pulmonary Resuscitation, or CPR, can sustain life until paramedics arrive by maintaining blood flow to the heart and brain. “Without oxygen-rich blood, permanent brain damage or death can occur in less than 8 minutes. After 10 minutes there is little chance of successful resuscitation. Even in modern urban settings the response times for professional rescuers commonly approach these time frames” (1). This explains why “effective bystander CPR, provided immediately after sudden cardiac arrest, can double or triple a person’s chance of survival” (2). In fact, close to 60% of adults in the US say they have taken CPR training (often due to school requirements) and 11% say they have used CPR in an actual emergency (3).

So why not develop a dedicated smartphone app to alert bystanders when someone nearby is suffering from a Sudden Cardiac Arrest? This is what Mark was getting at when we started this conversation back in April. Well it just so happens that such an app does exist. The PulsePoint mobile app “alerts CPR-trained bystanders to someone nearby having a sudden cardiac arrest that may require CPR. The app is activated by the local public safety communications center simultaneous with the dispatch of local fire and EMS resources” (4).

Reducing collapse-to-defibrillation times by increasing awareness of public access defibrillator (AED) locations through real-time mapping of nearby devices.

The PulsePoint approach is instructive to those of us applying technology to improve international humanitarian response. First, the app works within, not outside, existing institutions. When someone calls 911 to report a cardiac arrest, paramedics are still dispatched to the scene. At the same time, emergency operators use PulsePoint to alert registered bystanders in the area. Second, volunteers who receive an alert are provided with a map of nearby AEDs, i.e., additional “meta-data” important for rapid response. Third, training is key. Without CPR training, the “crowd” is not empowered to help. So Community Emergency Response Teams (CERTs) are important. Of course, not all needs require special expertise to be fulfilled, but preparedness still goes a long way.

That’s the number of calls that 75-year old social justice leader Anna Hazare received from people across India who supported his efforts to fight corruption. Two weeks earlier, he had invited India to join his movement by making “missed calls” to a local number. Missed calls, known as beeping or flashing, are calls that are intentionally dropped after ringing. The advantage of making missed call is that neither the caller or recipient is charged. This tactic is particularly common in emerging economies to avoid paying for air time or SMS. To build on this pioneering work, Anna and his team are developing a mobile petition tool called Crowdring, which turns a free “missed call” into a signature on a petition.

Communicating with disaster-affected communities is key for effective disaster response. Crowdring could be used to poll disaster affected communities. The service could also be used in combination with local community radio stations. The latter would broadcast a series of yes or no questions; ringing once would signify yes, twice would mean no. Some questions that come to mind:

Do you have enough drinking water?

Are humanitarian organizations doing a good job?

Is someone in your household displaying symptoms of cholera?

By receiving these calls, humanitarians would automatically be able to create a database of phone numbers with associated poll results. This means they could text them right back for more information or to arrange an in person meeting. You can learn more about Crowdring in this short video below.

“Since wars begin in the minds of men, it is in the minds of men that the defenses of peace must be constructed.” – UNESCO Constitution, 1945

Today, in Kenya, PeaceTXT is building the defenses of peace out of text messages (SMS). As The New York Times explains, PeaceTXT is developing a “text messaging service that sends out blasts of pro-peace messages to specific areas when trouble is brewing.” Launched by PopTech in partnership with the Kenyan NGO Sisi ni Amani (We are Peace), the Kenyan implementation of PeaceTXT uses mobile advertising to market peace and change men’s behaviors.

Conflicts are often grounded in the stories and narratives that people tell them-selves and in the emotions that these stories evoke. Narratives shape identity and the social construct of reality—we interpret our lives through stories. These have the power to transform or infect relationships and communities. As US-based PeaceTXT partner CureViolence (formerly CeaseFire) has clearly shown, violence propagates in much the same way as infectious diseases do. The good news is that we already know how to treat the later: by blocking transmission and treating the infected. This is precisely the approach taken by CureViolence to successfully prevent violence on the streets of Chicago, Baghdad and elsewhere.

The challenge? CureViolence cannot be everywhere at the same time. But the “Crowd” is always there and where the crowd goes, mobile phones often follow. PeaceTXT leverages this new reality by threading a social narrative of peace using mobile messages. Empirical research in public health (and mobile adver-tising) clearly demonstrates that mobile messages & reminders can change behaviors. Given that conflicts are often grounded in the narratives that people tell themselves, we believe that mobile messaging may also influence conflict behavior and possibly prevent the widespread transmission of violent mindsets.

To test this hypothesis, PopTech partnered with Sisi ni Amani in 2011 to pilot and assess the use of mobile messaging for violence interruption and prevention since SNA-K had already been using mobile messaging for almost three years to promote peace, raise awareness about civic rights and encourage recourse to legal instruments for dispute resolution. During the twelve months leading up to today’s Presidential Elections, the Kenyan NGO Sisi ni Amani (SNA-K) has worked with PopTech and PeaceTXT partners (Medic Mobile, QCRI, Ushahidi & CureViolence) to identify the causes of peace in some of the country’s most conflict-prone communities. Since wars begin in the minds of men, SNA-K has held dozens of focus groups in many local communities to better understand the kinds of messaging that might make would-be perpetrators think twice before committing violence. Focus group participants also discussed the kinds of messaging needed to counter rumors. Working with Ogilvy, a global public relations agency with expertise in social marketing, SNA-K subsequently codified the hundreds of messages developed by the local communities to produce a set of guidelines for SNA-K staff to follow. These guidelines describe what types of messages to send to whom, where and when depending on the kinds of tensions being reported.

In addition to organizing these important focus groups, SNA-K literally went door-to-door in Kenya’s most conflict-prone communities to talk with residents about PeaceTXT and invite them to subscribe to SNA-Ks free SMS service. Today, SNA-K boasts over 60,000 SMS subscribers across the country. Thanks to Safaricom, the region’s largest mobile operator, SNA-K will be able to send out 50 million text messages completely for free, which will significantly boost the NGO’s mobile reach during today’s elections. And thanks to SNA-K’s customized mobile messaging platform built by the Praekelt Foundation, the Kenyan NGO can target specific SMS’s to individual subscribers based on their location, gender and demographics. In sum, as CNN explains, “the intervention combines targeted SMS with intensive on-the-ground work by existing peace builders and community leaders to target potential flashpoints of violence.”

The partnership with Pop-Tech enabled SNA-K to scale thanks to the new funding and strategic partnerships provided by PopTech. Today, PeaceTXT and Sisi ni Amani have already had positive impact in the lead up to today’s important elections. For example, a volatile situation in Dandora recently led to the stabbing of several individuals, which could have resulted in a serious escalation of violence. So SNA-K sent the following SMS:

“Tu dumisha amani!” means “Lets keep the peace!” SNA-K’s local coordinator in Dandore spoke with a number of emotionally distraught and (initially) very angry individuals in the area who said they had been ready to mobilizing and take revenge. But, as they later explained, the SMS sent out by SNA-K made them think twice. They discussed the situation and decided that more violence wouldn’t bring their friend back and would only bring more violence. They chose to resolve the volatile situation through mediation instead.

In Sagamian, recent tensions over land issues resulted in an outbreak of violence. So SNA-K sent the following message:

Those involved in the fighting subsequently left the area, telling SNA-K that they had decided not to fight after receiving the SMS. What’s more, they even requested that additional messages to be sent. Sisi ni Amani has collected dozens of such testimonials, which suggest that PeaceTXT is indeed having an impact. Historian Geoffrey Blainey once wrote that “for every thousand pages on the causes of war, there is less than one page directly on the causes of peace.” Today, the PeaceTXT Kenya & SNAK partnership is making sure that for every one SMS that may incite violence, a thousand messages of peace, calm and solidarity will follow to change the minds of men. Tudumishe amani!

Disaster response apps have multiplied in recent years. I’ve been reviewing the most promising ones and have found that many cater to professional responders and organizations. While empowering paid professionals is a must, there has been little focus on empowering the real first responders, i.e., the disaster-affected communities themselves. To this end, there is always a dramatic mismatch in demand for responder services versus supply, which is why crises are brutal audits for humanitarian organizations. Take this Red Cross survey, which found that 74% of people who post a need on social media during a disaster expect a response within an hour. But paid responders cannot be everywhere at the same time during a disaster. The response needs to be decentralized and crowdsourced.

In contrast to paid responders, the crowd is always there. And most survivals following a disaster are thanks to local volunteers and resources, not external aid or relief. This explains why FEMA Administrator Craig Fugate has called on the public to become a member of the team. Decentralization is probably the only way for emergency response organizations to improve their disaster audits. As many seasoned humanitarian colleagues of mine have noted over the years, the majority of needs that materialize during (and after) a disaster do not require the attention of paid disaster responders with an advanced degree in humanitarian relief and 10 years of experience in Haiti. We are not all affected in the same way when disaster strikes, and those less affected are often very motivated and capable at responding to the basic needs of those around them. After all, the real first responders are—and have always been—the local communities themselves, not the Search and Rescue Teams that parachutes in 36 hours later.

In other words, local self-organized action is a natural response to disasters. Facilitated by social capital, self-organized action can accelerate both response & recovery. A resilient community is therefore one with ample capacity for self-organization. To be sure, if a neighborhood can rapidly identify local needs and quickly match these with available resources, they’ll rebound more quickly than those areas with less capacity for self-organized action. The process is a bit like building a large jigsaw puzzle, with some pieces standing for needs and others for resources. Unlike an actual jigsaw puzzle, however, there can be hundreds of thousands of pieces and very limited time to put them together correctly.

This explains why I’ve long been calling for a check-in & match.com smartphone app for local collective disaster response. The talk I gave (above) at Where 2.0 in 2011 highlights this further as do the blog posts below.

This “MatchApp” could rapidly match hyper local needs with resources (material & informational) available locally or regionally. Check-in’s (think Foursquare) can provide an invaluable function during disasters. We’re all familiar with the command “In case of emergency break glass,” but what if: “In case of emergency, then check-in”? Checking-in is space- and time-dependent. By checking in, I announce that I am at a given location at a specific time with a certain need (red button). This means that information relevant to my location, time, user-profile (and even vital statistics) can be customized and automatically pushed to my MatchApp in real-time. After tapping on red, MatchApp prompts the user to select what specific need s/he has. (Yes, the icons I’m using are from the MDGs and just placeholders). Note that the App we’re building is for Androids, not iPhones, so the below is for demonstration purposes only.

But MatchApp will also enable users who are less (or not) affected by a disaster to check-in and offer help (by tapping the green button). This is where the match-making algorithm comes to play. There are various (compatible options) in this respect. The first, and simplest, is to use a greedy algorithm. This algorithm select the very first match available (which may not be the most optimal one in terms of location). A more sophisticated approach is to optimize for the best possible match (which is a non-trivial challenge in advanced computing). As I’m a big fan of Means of Exchange, which I have blogged about here, MatchApp would also enable the exchange of goods via bartering–a mobile eBay for mutual-help during disasters.

Once a match is made, the two individuals in question receive an automated alert notifying them about the match. By default, both users’ identities and exact locations are kept confidential while they initiate contact via the app’s instant messaging (IM) feature. Each user can decide to reveal their identity/location at any time. The IM feature thus enables users to confirm that the match is indeed correct and/or still current. It is then up to the user requesting help to share her or his location if they feel comfortable doing so. Once the match has been responded to, the user who received help is invited to rate the individual who offered help (and vice versa, just like the Uber app, depicted on the left below).

As a next generation disaster response app, MatchApp would include a number of additional data entry features. For example, users could upload geo-tagged pictures and video footage (often useful for damage assessments). In terms of data consumption and user-interface design, MatchApp would be modeled along the lines of the Waze crowdsourcing app (depicted on the right above) and thus designed to work mostly “hands-free” thanks to a voice-based interface. (It would also automatically sync up with Google Glasses).

In terms of verifying check-in’s and content submitted via MatchApp, I’m a big fan of InformaCam and would thus integrate the latter’s meta-data verification features into MatchApp: “the user’s current GPS coordinates, altitude, compass bearing, light meter readings, the signatures of neighboring devices, cell towers, and wifi networks; and serves to shed light on the exact circumstances and contexts under which the digital image was taken.” I’ve also long been interested in peer-to-peer meshed mobile communication solutions and would thus want to see an integration with the Splinternet app, perhaps. This would do away with the need for using cell phone towers should these be damaged following a disaster. Finally, MatchApp would include an agile dispatch-and-coordination feature to allow “Super Users” to connect and coordinate multiple volunteers at one time in response to one or more needs.

In conclusion, privacy and security are a central issue for all smartphone apps that share the features described above. This explains why reviewing the security solutions implemented by multiple dating websites (especially those dating services with a strong mobile component like the actual Match.com app) is paramount. In addition, reviewing security measures taken by Couchsurfing, AirBnB and online classified adds such as Craig’s List is a must. There is also an important role for policy to play here: users who submit false misinformation to MatchApp could be held accountable and prosecuted. Finally, MatchApp would be free and open source, with a hyper-customizable, drag-and-drop front- and back-end.

The purpose of PeaceTXT is to use mobile messaging (SMS) to catalyze behavior change vis-a-vis peace and conflict issues for the purposes of violence prevention. You can read more about our pilot project in Kenya here and here. We’re hoping to go live next month with some initial trials. In the meantime, we’ve been busy doing research to develop an appropriate monitoring and evaluation strategy. As is often the case in this new innovative initiatives, we have to look to other fields for insights, which is why my colleague Peter van der Windt recently shared this peer-reviewed study entitled: “Mobile Phone Technologies Improve Adherence to Antiretroviral Treatment in a Resource-Limited Setting: A Randomized Con-trolled Trial of Text Message Reminders.”

The objective of the study was to test the “efficacy of short message service (SMS) reminders on adherence to Antiretroviral Treatment (ART) among patients attending a rural clinic in Kenya.” The authors used a Randomized Control Trial (RCT) of “four SMS reminders interventions with 48 weeks of follow-up.” Over four hundred patients were enrolled in the trial and “randomly assigned to a control group or one of the four intervention groups. Participants in the intervention groups received SMS reminders that were either short or long and sent at a daily or weekly frequency.”

The four different text message interventions were “chosen to address different barriers to adherence such as forgetful- ness and lack of social support. Short messages were meant to serve as a simple reminder to take medications, whereas long messages were meant to provide additional support. Daily messages were close to the frequency of medication usage, whereas weekly messages were meant to avoid the possibility that very frequent text messages would be habituating.” The SMS content was developed after extensive consultation with clinic staff and the messages were “sent at 12 p.m., rather than twice daily (during dosing times) to avoid excess reliance on the accuracy of the SMS software.”

The results of the subsequent statistical analysis reveal that “53% of participants receiving weekly SMS reminders achieved adherence of at least 90% during the 48 weeks of the study, compared with 40% of participants in the control group. Participants in groups receiving weekly reminders were also significantly less likely to experience treatment interruptions exceeding 48 hours during the 48-week follow-up period than participants in the control group.” Interestingly, “adding words of encouragement in the longer text message reminders was not more effective than either a short reminder or no reminder.” Furthermore, it is worth noting that “weekly reminders improved adherence, whereas daily remin-ders did not. Habituation, or the diminishing of a response to a frequently repeated stimulus, may explain this finding. Daily messages might also have been considered intrusive.”

In sum, “despite SMS outages, phone loss, and a rural population, these results suggest that simple SMS interventions could be an important strategy to sustaining optimal ART response.” In other words, SMS reminders can serve as an important tool to catalyze positive behavior change in resource-limited settings. Several insights from this study are going to be important for us to consider in our PeaceTXT project. So if you know of any other relevant studies we should be paying attention to, then please let us know. Thank you!

Friends Peter van der Windt and Gregory Asmolov are two of the sharpest minds I know when it comes to crowdsourcing crisis information and crisis response. So it was a real treat to catch up with them in Berlin this past weekend during the “ICTs in Limited Statehood” workshop. An edited book of the same title is due out next year and promises to be an absolute must-read for all interested in the impact of Information and Communication Technologies (ICTs) on politics, crises and development.

I blogged about Gregory’s presentation following last year’s workshop, so this year I’ll relay Peter’s talk on research design and methodology vis-a-vis the collection of security incidents in conflict environments using SMS. Peter and mentor Macartan Humphreys completed their Voix des Kivus project in the DRC last year, which ran for just over 16 months. During this time, they received 4,783 text messages on security incidents using the FrontlineSMS platform. These messages were triaged and rerouted to several NGOs in the Kivus as well as the UN Mission there, MONUSCO.

How did they collect this information in the first place? Well, they considered crowdsourcing but quickly realized this was the wrong methodology for their project, which was to assess the impact of a major conflict mitigation program in the region. (Relaying text messages to various actors on the ground was not initially part of the plan). They needed high-quality, reliable, timely, regular and representative conflict event-data for their monitoring and evaluation project. Crowdsourcing is obviously not always the most appropriate methodology for the collection of information—as explained in this blog post.

Peter explained the pro’s and con’s of using crowdsourcing by sharing the framework above. “Knowledge” refers to the fact that only those who have knowledge of a given crowdsourcing project will know that participating is even an option. “Means” denotes whether or not an individual has the ability to participate. One would typically need access to a mobile phone and enough credit to send text messages to Voix des Kivus. In the case of the DRC, the size of subset “D” (no knowledge / no means) would easily dwarf the number of individuals comprising subset “A” (knowledge / means). In Peter’s own words:

“Crowdseeding brings the population (the crowd) from only A (what you get with crowdsourcing) to A+B+C+D: because you give phones&credit and you go to and inform the phoneholds about the project. So the crowd increases from A to A+B+C+D. And then from A+B+C+D one takes a representative sample. So two important benefits. And then a third: the relationship with the phone holder: stronger incentive to tell the truth, and no bad people hacking into the system.”

In sum, Peter and Macartan devised the concept of “crowdseeding” to increase the crowd and render that subset a representative sample of the overall population. In addition, the crowdseeding methodology they developed genera-ted more reliable information than crowdsourcing would have and did so in a way that was safer and more sustainable.

Peter traveled to 18 villages across the Kivus and in each identified three representatives to serve as the eyes and years of the village. These representatives were selected in collaboration with the elders and always included a female representative. They were each given a mobile phone and received extensive training. A code book was also shared which codified different types of security incidents. That way, the reps simply had to type the number corresponding to a given incident (or several numbers if more than one incident had taken place). Anyone in the village could approach these reps with relevant information which would then be texted to Peter and Macartan.

The table above is the first page of the codebook. Note that the numerous security risks of doing this SMS reporting were discussed at length with each community before embarking on the selection of 3 village reps. Each community decided to voted to participate despite the risks. Interestingly, not a single village voted against launching the project. However, Peter and Macartan chose not to scale the project beyond 18 villages for fear that it would get the attention of the militias operating in the region.

A local field representative would travel to the villages every two weeks or so to individually review the text messages sent out by each representative and to verify whether these incidents had actually taken place by asking others in the village for confirmation. The fact that there were 3 representatives per village also made the triangulation of some text messages possible. Because the 18 villages were randomly selected as part the randomized control trial (RCT) for the monitoring and evaluation project, the text messages were relaying a representative sample of information.

But what was the incentive? Why did a total of 54 village representatives from 18 villages send thousands of text messages to Voix des Kivus over a year and a half? On the financial side, Peter and Macartan devised an automated way to reimburse the cost of each text message sent on a monthly basis and in addition provided an additional $1.5/month. The only ask they made of the reps was that each had to send at least one text message per week, even if that message had the code 00 which referred to “no security incident”.

The figure above depicts the number of text messages received throughout the project, which formally ended in January 2011. In Peter’s own words:

“We gave $20 at the end to say thanks but also to learn a particular thing. During the project we heard often: ‘How important is that weekly $1.5?’ ‘Would people still send messages if you only reimburse them for their sent messages (and stop giving them the weekly $1.5)?’ So at the end of the project […] we gave the phone holder $20 and told them: the project continues exactly the same, the only difference is we can no longer send you the $1.5. We will still reimburse you for the sent messages, we will still share the bulletins, etc. While some phone holders kept on sending textmessages, most stopped. In other words, the financial incentive of $1.5 (in the form of phonecredit) was important.”

Peter and Macartan have learned a lot during this project, and I urge colleagues interested in applying their project to get in touch with them–I’m happy to provide an email introduction. I wish Swisspeace’s Early Warning System (FAST) had adopted this methodology before running out of funding several years ago. But the leadership at the time was perhaps not forward thinking enough. I’m not sure whether the Conflict Early Warning and Response Network (CEWARN) in the Horn has fared any better vis-a-vis demonstrated impact or lack thereof.

To learn more about crowdsourcing as a methodology for information collection, I recommend the following three articles: